Hybrid Machine Learning Models for Predicting the Impact of Light Wavelengths on Algal Growth in Freshwater Ecosystems

This study examines the influence of light wavelengths on the growth dynamics of five algal genera (Chlorella sp., Volvox sp., Gloeocapsa sp., Microspora sp., and Mougeotia sp.) in freshwater systems, using machine learning to optimize growth models. Natural light yielded the highest algal proliferation, increasing the total count from 90 to 1390 cells/mL in 30 days. Filtered wavelengths showed that blue light most effective (840 cells/mL), followed by red (490 cells/mL) and yellow (200 cells/mL), while green light minimally impacted growth (160 cells/mL). Genera-specific responses revealed that Gloeocapsa sp. and Mougeotia sp. thrived the most under blue light (240 and 750 cells/mL, respectively), with red and blue wavelengths generally enhancing growth across genera. Machine learning models achieved high accuracy (R2 > 0.96 for total growth and R2 > 0.8 for genera-specific and wavelength-based models), refining growth kinetics. These results suggest that spectral manipulation limiting blue/red wavelengths in water treatment to curb blooms while leveraging natural light for biofuel cultivation could optimize algal management. The integration of empirical data with machine learning offers a robust framework for predictive modeling in algal research and industrial applications.